METHODS: Circulating CD8+ T cells were analysed for differentiation status (CD45RO, CCR7), markers of activation (CD69 and CD25) and proliferation (Ki-67) in 14 newly diagnosed GCA patients and 18 healthy controls by flow cytometry. Proliferative capacity of CD8+ T cells upon anti-CD3 and anti-CD3/28 in vitro stimulation was assessed. Single-cell RNA sequencing of peripheral blood mononuclear cells of patients and controls (n = 3 each) was performed for mechanistic insight. Immunohistochemistry was used to detect CD3, CD8, Ki-67, TNF-α and IFN-γ in GCA-affected tissues.
RESULTS: GCA patients had decreased numbers of circulating effector memory CD8+ T cells but the percentage of Ki-67-expressing effector memory CD8+ T cells was increased. Circulating CD8+ T cells from GCA patients demonstrated reduced T cell receptor activation thresholds and displayed a gene expression profile that is concurrent with increased proliferation. CD8+ T cells were detected in GCA temporal arteries and aorta. These vascular CD8+ T cells expressed IFN-γ but not Ki-67.
CONCLUSION: In GCA, circulating effector memory CD8+ T cells demonstrate a proliferation-prone phenotype. The presence of CD8+ T cells in inflamed arteries seems to reflect recruitment of circulating cells rather than local expansion. CD8+ T cells in inflamed tissues produce IFN-γ, which is an important mediator of local inflammatory responses in GCA.
METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.
RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.
DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.
RESULTS: We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control.
CONCLUSIONS: DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.
METHODS: In this study, we established persistently NDV-infected EJ28 bladder cancer cells, designated as EJ28P. Global transcriptomic analysis was subsequently carried out by microarray analysis. Differentially expressed genes (DEGs) between EJ28 and EJ28P cells identified by the edgeR program were further analysed by Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) analyses. In addition, the microarray data were validated by RT-qPCR.
RESULTS: Persistently NDV-infected EJ28 bladder cancer cells were successfully established and confirmed by flow cytometry. Microarray analysis identified a total of 368 genes as differentially expressed in EJ28P cells when compared to the non-infected EJ28 cells. GSEA revealed that the Wnt/β-catenin and KRAS signalling pathways were upregulated while the TGF-β signalling pathway was downregulated. Findings from this study suggest that the upregulation of genes that are associated with cell growth, pro-survival, and anti-apoptosis may explain the survivability of EJ28P cells and the development of persistent infection of NDV.
CONCLUSIONS: This study provides insights into the transcriptomic changes that occur and the specific signalling pathways that are potentially involved in the development and maintenance of NDV persistency of infection in bladder cancer cells. These findings warrant further investigation and is crucial towards the development of effective NDV oncolytic therapy against cancer.